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Record W2011475935 · doi:10.1177/154405910508400808

Oxygen Inhibition in Dental Resins

2005· article· en· W2011475935 on OpenAlexafffund
Marc A. Gauthier, I. Stangel, T.H. Ellis, X. X. Zhu

Bibliographic record

VenueJournal of Dental Research · 2005
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesAmerican Association for Dental, Oral, and Craniofacial Research
KeywordsPolymerizationOxygenViscosityDiffusionPolymerMonomerRaman spectroscopyChemistryPolymer chemistryChemical engineeringMaterials sciencePhotochemistryOrganic chemistryComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Oxygen inhibits free radical polymerization and yields polymers with uncured surfaces. This is a concern when thin layers of resin are being polymerized, or in circumstances where conventional means of eliminating inhibition are inappropriate. In this study, we tested the hypothesis that viscosity, filler content, and polymerization temperature modify oxygen diffusion in the resin or the reactivity of radical species, and affect the degree of conversion near the surface. Confocal Raman micro-spectroscopy was used to measure monomer conversion from the surface to the bulk of cured resins. Increased viscosity was shown to limit oxygen diffusion and increase conversion near the surface, without necessarily modifying the depth of inhibition. The filler material was shown to increase, simultaneously, oxygen diffusivity and the viscosity of the resin, which have opposite effects on conversion. Polymerization at a temperature above approximately 110 degrees C was shown to eliminate oxygen inhibition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.413
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations176
Published2005
Admission routes2
Has abstractyes

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